The long-term goal of this work is to rationally manipulate G protein-coupled receptor (GPCRs) activation with the hope to develop novel therapeutic approaches in this major family of transmembrane receptors and drug targets. Our hypothesis is that different GPCRs share common elements of their signal transduction mechanism. If so, it should be possible to compare and contrast their sequences to reveal functionally relevant amino acid variation patterns, some that are common to all receptors and indicative of shared mechanisms, and others that are unique to some branches of the family and indicative of ligand-specific mechanisms. This principle led to a general algorithm, called the Evolutionary Trace (ET) that correlates residue substitutions with evolutionary divergences and thus ranks the evolutionary importance of a protein's residues. It was shown, in the past funding period that ET could search protein structure for functional sites and their specificity determinants on a large-scale. Mutations guided to ET's top-ranked residues (so-called trace residues) repeatedly blocked, separated, mimicked, or rewired protein interactions in numerous experimental case studies in GPCRs and in other proteins. In parallel, high-throughput ET analyses suggested that trace residues have distinctive and quantifiable and proteome-wide properties in terms of structural clustering, biophysical interaction, and functional specificity. This proposal builds on these observations by pursuing three specific aims: 1. To redirect ligand binding in bioamine receptors. 2. To redesign ligand binding and dimerization in metabotropic glutamate receptors. 3. To identify transmembrane GPCR response modulators on a large scale. The outcome should reveal new aspects of the molecular basis of signaling in an important family of pharmaceutical targets. It will also link sequence and structure genomics databases to the molecular basis of function and to the rational re-design of protein interactions - key steps towards manipulating cellular pathways. It will benefit human health by providing new approaches to rational drug design and by enhancing the diagnostic value of SNP analysis and human genotyping.